The present invention relates to the field of wireless communication systems, in particular, coded performance of multi-user systems in the presence of adjacent channel interference (ACI).
Spectral spreading is problem in systems in which the communication channel is shared by several users, wherein one channel “bleeds over” into another channel, which is referred to as ACI. This ACI problem worsens as the spacing between channels decreases.
In multi-user systems, for example, frequency division multiplexed (FDM) systems, ACI can seriously impair performance, especially when high bandwidth efficiencies are desired. In order to achieve high bandwidth/spectral efficiencies, the frequency separation between the adjacent channels must be reduced, causing an increase in ACI and resulting in performance degradation. A common practice to improve the performance is by applying interference cancellation at the receiver however, this entails an increase in complexity.
Alternatively single-user detection can be applied, in which the performance could be improved through a more judicious choice of the modulation and channel (error correction) coding, for example, through the use of continuous phase modulation (CPM). Through a careful design of the CPM phase pulse and selection of the remaining modulation parameters, power spectrum of the CPM signal can be shaped to improve the resistance to ACI. CPM can also provide excellent energy efficiency, for instance, by using binary convolutional codes, ring-convolutional codes, low density parity check (LDPC) codes, irregular, repeat-accumulate codes and more recently, extended BCH codes.
Pulse shape designs for single-user uncoded CPM and pulse shapes designed using higher order polynomials for binary CPM are known in the art. These are designed to maximize the minimum squared Euclidian distance for a specific effective signal bandwidth. Similarly, higher order polynomials are also used to design pulse shapes for partial response binary CPM. These pulse shapes are designed to maximize the minimum Euclidean distance of the uncoded modulation, subject to the constraint that the resulting power spectrum stays below a pre-defined mask. However, the minimum Euclidian distance based design known in the art is suboptimal for the coded multi-user systems since it does not explicitly consider the effect of ACI and the channel code.
All the methods discussed in the preceding paragraph are limited to uncoded, single user systems. The power and efficiency advantages offered by CPM generally come at the cost of high complexity receivers because of the non-linear nature of the modulation format. This will be described further using a system model with a transmitter and a receiver with reference to FIGS. 1-2.
FIG. 1 illustrates a conventional transmitter 100 and channel for a conventional multi-user system.
As illustrated in FIG. 1, transmitter 100 includes a transmitting source 102, a transmitting source 104 and a transmitting source 106. Transmitting source 102 further includes a bit source 108, a binary convolutional coder 110, an S-random interleaver 112, a CPM 114, a mixer 116, a multiplier 118. Transmitting source 104 further includes a bit source 120, a binary convolutional coder 122, an S-random interleaver 124, a CPM 126, a mixer 128, a multiplier 130. Transmitting source 106 further includes a bit source 132, a binary convolutional coder 134, an S-random interleaver 136, a CPM 138, a mixer 140, a multiplier 142. An adder 144 models the ACI and an adder 146 models the additive white Gaussian noise i.e. AWGN. Transmitter 100 may not strictly include adder 144 and adder 146. Adder 144 and adder 146 can be thought of as the channel, but they are grouped in with transmitter 100 for convenience.
In this figure, each of bit source 108, binary convolutional coder 110, S-random interleaver 112, CPM 114, mixer 116, multiplier 118, bit source 120, binary convolutional coder 122, S-random interleaver 124, CPM 126, mixer 128, multiplier 130, bit source 132, binary convolutional coder 134, S-random interleaver 136, CPM 138, mixer 140, multiplier 142, adder 144 and adder 146 are illustrated as distinct devices. However, at least one of bit source 108, binary convolutional coder 110, S-random interleaver 112, CPM 114, mixer 116, multiplier 118, bit source 120, binary convolutional coder 122, S-random interleaver 124, CPM 126, mixer 128, multiplier 130, bit source 132, binary convolutional coder 134, S-random interleaver 136, CPM 138, mixer 140, multiplier 142, adder 144 and adder 146 may be combined as a unitary device.
Transmitting sources 102, 104 and 106 are shown in FIG. 1, however, there could be K users sharing the same channel, where K>1, resulting in interference. Operation of transmitting source 102 is described further in detail.
Bit source 108 is operable to provide information data bits signal 148 for transmitting source 102 to binary convolutional coder 110. Non-limiting examples for bit source 108 include data, images, video, audio, etc.
Binary convolutional coder 110 is operable to receive information data bits signal 148 and provides encoded data bits signal 150 to S-random interleaver 112. Binary convolutional coder 110 provides forward error correction by adding redundancy to information data bits signal 148. Forward error correction (FEC) improves the capacity of a channel by adding some carefully designed redundant information to the data being transmitted through the channel. Binary convolution coding is a form of channel coding to add this redundant information to the data.
S-random interleaver 112 is operable to scramble the encoded data bits 150 by rearranging the bit sequence in order to improve error rate performance and lower the error floors. Interleaving is a process of rearranging the ordering of a data sequence in a one to one deterministic format. The inverse of this process is calling deinterleaving, which restores the received sequence to its original order. Interleaving is used to enhance the error correcting capability of coding. An S-random interleaver (where S=1, 2, 3 . . . ) is a “semi-random” interleaver, which changes the order of the data sequence of incoming input bits, and generally provides the permuted data sequence in the form of an interleaving matrix.
CPM modulator 114 is operable to receive the data symbols after bit-to-symbol mapping of scrambled codebits 152. CPM modulator 114 generates a CPM signal 154 such that its phase transitions do not exhibit any discontinuity from one symbol epoch to the next (i.e. the phase of the modulated signal varies continuously with time). The phase continuity and smooth phase transitions enable the transmitted signal to have relatively small power spectral side-lobes. CPM modulator 114 provides CPM signal 154 to frequency mixer 116.
Frequency mixer 116 is operable to receive CPM signal 154 from CPM 114 and mix CPM signal 154 with signal 156 to generate mixed signal 158. Signal 156 may be represented as exp(j2πf1t). Frequency mixer 116 frequency translates CPM signal 154 from CPM 114. For example, if the center frequency of CPM signal 154 at the output of CPM 114 is f0, then, the center frequency of CPM signal 154 at the output of frequency mixer 116 becomes (f0+f1).
Multiplier 118 is operable to receive mixed signal 158 from mixer 116 and multiply it with signal 160. Signal 160 may be represented as σ1, which is used to adjust the power level of signal 158.
Transmitting sources 104 and 106 operate in the similar manner as transmitting source 102, therefore are not described here. CPM signal 170 from transmitting source 104 is mixed with exp(j2πf2t) and multiplied with σ2 before going to adder 144. Similarly, CPM signal 188 from transmitting source 106 is mixed with exp(j2πfKt) and multiplied with σK before going to adder 144, where K>1.
Adder 144 is operable to receive output signal 180 from transmitting source 104 and output signal 196 from transmitting source 106 along with output signal 162 from transmitting source 102 and provides composite CPM signal 197 to adder 146. In case of more than three transmitting sources, adder 144 is operable to receive outputs signals from those transmitting sources as well. Adder 144 models the effect of the ACI, which occurs in bandwidth limited communication systems.
Composite CPM signal 197 out of adder 144 is added with another signal 198 by adder 146. Signal 198 may be represented by n(t)˜η(0,N0). Signal 198 is added to model Additive White Gaussian Noise (AWGN) for transmission through the channel. Output signal 199 of adder 146 represents a transmitted signal from a multi-user system, which may be received by a receiver of the multi-user system, as will now be discussed with reference to FIG. 2.
FIG. 2 illustrates a conventional receiver 200 for a user k of a multi-user system.
As illustrated in FIG. 2, receiver 200 includes a mixer 202, a matched filter bank 204, a CPM detector 206, an S-random deinterleaver 208, a binary convolutional decoder 210 and an S-random interleaver 212. In this illustration, each of mixer 202, matched filter bank 204, CPM detector 206, S-random deinterleaver 208, binary convolutional decoder 210 and S-random interleaver 212 are illustrated as distinct devices. However, at least one of mixer 202, correlator 204, CPM detector 206, S-random deinterleaver 208, binary convolutional decoder 210 and S-random interleaver 212 may be combined as a unitary device.
Mixer 202 is operable to receive channel output signal 214 from a transmitting source, for example, channel output signal 199 from transmitter 100, and mix channel output signal 214 with signal 216 to output signal 218. Signal 216 may be represented as exp(−j2πfkt)/σk for receiver k. Mixer 202 performs the reverse operation of the mixer and multiplier of the transmitting source in order to frequency translate and apply amplitude correction before recovering the transmitted signal.
Matched filter bank 204 is operable to receive signal 218 and provide signal 220 to CPM detector 206. Matched filter bank 204 may operate to filter the received signal 218 with N possible reference signals, where N is a finite integer number and depends on the choice of the CPM modulation parameters. Matched filter bank 204 provides a matrix, which contains statistical indication as to which of the possible transmitted signals may be the received signal 218.
CPM detector 206 is operable to receive signal 220 from matched filter bank 204 and a priori information 232 from S-random interleaver 212. CPM detector 206 uses the statistics provided by matched filter bank 204 within signal 220 to perform decoding using a decoding algorithm like Viterbi or BCJR algorithm for providing an estimate of the transmitted symbols (or equivalently of the transmitted codebits). CPM detector 206 may also operate to receive scrambled a priori probability information 232 of the transmitted codebits being either a 1 or a 0 (i.e. bits generated by the convolutional code) from S-random interleaver 212 in order to provide a better estimate of the transmitted signal.
Every interleaver has a corresponding deinterleaver, which acts on the interleaved data sequence and restores it to its original order. S-random deinterleaver 208 is operable to receive probability estimates of the transmitted codebits being either a 1 or a 0, in the form of extrinsic information 224 from CPM detector 206 and provides descrambled probability estimates 226 to binary convolutional decoder 210.
Binary convolutional decoder 210 is operable to decode using descrambled probability estimates 226 by using a decoding algorithm, for example, Viterbi, BCJR and provides a bit sequence 228 and updated probability estimates of the transmitted codebits 230 being either a 1 or a 0.
S-random interleaver 212 is operable to improve the error rate performance by feeding the scrambled probability estimates as a priori information 232 back to CPM detector 206. The goal of receiver 200 is to process the received signal such that bit sequence 228 recovered by receiver 200 matches the bit source provided by transmitting source k.
The first pass through CPM detector 206, S-random deinterleaver 208 and binary convolutional decoder 210 provides an estimate of bit sequence 228 which may match with the transmitted bit source. The operation of S-random interleaver 212 providing feedback to CPM detector 206 improves the signal estimate with successive iterations though the feedback loop, until recovered information matches information provided by the transmitting source or until a maximum number of iterations are performed, whichever occurs first.
The operation of a conventional transmitting source, which involved encoding, scrambling and modulation of a bit source for transmission via a channel of a multi-user system was discussed previously with respect to FIG. 1. The operation of a conventional receiver, which involved correlating, descrambling and decoding of the received bit source from the channel of the multi-user system to recover the original bit source, was discussed with respect to FIG. 2.
The phase response used in the CPM modulator and power spectral density (PSD) of the signal at the modulator output which is transmitted through a multi-user system and received by a receiver will now be discussed with reference to FIGS. 3-4.
FIG. 3 illustrates a graph 300 for CPM phase response with t/T for a conventional rectangular (REC) and raised-cosine (RC) pulses.
As illustrated in the figure, a y-axis 302 represents CPM phase response in radians and an x-axis 304 represents time normalized by symbol duration or time multiplied by symbol rate (t/T). CPM phase response is represented by a function q(t). The CPM phase response is obtained by integrating the CPM pulse shape over time. Graph 300 includes a curve 306, which represents the phase response qRE(t) for a rectangular (REC) pulse shape and a curve 308 which represents the phase response qRC(t) for a raised cosine RC pulse.
The purpose of function q(t) is to smooth out the phase transitions of the modulated signal. A more gradual variation of q(t) with time results in the modulated signal having smaller spectral side-lobes. Curves 306 and 308 provide a different power spectrum. Curves 306 and 308 are responses from a conventional CPM like CPM 114 or CPM 126 as described in FIG. 1.
FIG. 4 illustrates a graph 400 for PSD with frequency/symbol rate for conventional REC and RC pulses.
As illustrated in the figure, a y-axis 402 represents PSD in dB and an x-axis 404 represents frequency/symbol rate. Graph 400 includes a curve 406, which represents the PSD for a REC pulse and a curve 408 which represents the PSD for a RC pulse.
It is obvious from curve 406 that the CPM signal obtained from a modulator using REC pulse shapes have PSDs with a narrower main lobe and larger side lobes. Similarly, curve 408 represents the CPM signal obtained from a modulator using RC pulse shapes, which have PSDs with relatively smaller side-lobes, but wider main lobes.
It was shown using FIGS. 3-4, how conventional RC and REC pulses perform in a multi-user system in the presence of ACI. A narrow spectral main lobe (as exhibited by CPM signals using REC pulse shapes) can allow the adjacent users to be spaced closer, which would allow a more bandwidth efficient transmission. However, if the spectral side lobes are large, then the interference caused to neighboring users is more, which causes performance degradation. Hence smaller or rapidly decaying spectral side lobes (as exhibited by CPM signals using RC pulse shapes) are desirable. Conversely, since these signals have a wider main lobe, the adjacent carriers would have to be spaced further apart to minimize performance degradation due to ACI. This however reduces the bandwidth efficiency of the communication system.
In order to improve the coded performance of multi-user systems affected by ACI and to also provide higher bandwidth efficiencies; it is desirable for the signal PSD to have a narrow main lobe in order to allow signaling at higher spectral efficiencies by reducing the separation between the adjacent carriers. Additionally, it is advantageous to have a fast spectral roll-off (i.e., small side lobes) in order to minimize interference to neighboring carriers. There is a tradeoff between the width of the PSD main lobe and the rate of decay of the side lobes.
What is needed is a pulse shape that addresses the tradeoff between the width of the PSD main lobe and the rate of decay of the side lobe in a multi-user system to maximize the channel capacity but minimize the channel interference.